Update README.md

README.md

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 ### 🎯 Conclusion & Technical Achievements
-Testing the complete pipeline confirmed that the system goes far beyond matching raw colors. It successfully extracts and matches abstract architectural concepts and stylistic intent. The backend logic proved highly reliable and was successfully deployed as a fully interactive Hugging Face Space frontend application, demonstrating a complete end-to-end Machine Learning lifecycle.
+Testing the complete pipeline confirmed that the system goes far beyond matching raw colors. It successfully extracts and matches abstract architectural concepts and stylistic intent. The backend logic proved highly reliable and was successfully deployed as a fully interactive Hugging Face Space frontend application, demonstrating a complete end-to-end Machine Learning lifecycle.
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+## 📁 Project Resources
+For a complete technical deep dive, the core development files and generated datasets are available directly within this repository:
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+* **[📓 Colab (A3.ipynb)](https://huggingface.co/spaces/matanzig/Interior-Design-GenAI/blob/main/A3.ipynb):** Contains the full algorithmic workflow, including the comprehensive Exploratory Data Analysis (EDA), CLIP feature extraction, K-Means clustering, and t-SNE visualizations.
+* **[🗄️ Embeddings Database (interior_embeddings.parquet)](https://huggingface.co/spaces/matanzig/Interior-Design-GenAI/resolve/main/interior_embeddings.parquet):** The pre-computed 512-dimensional CLIP embeddings matrix, serving as the foundational knowledge base for the recommendation engine's vector search.
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